Automating the Process of Payer Pre-Approval in Healthcare
Are machines taking over your diagnosis and treatment? The short answer is, yes.
There is unprecedented innovation happening in healthcare, where artificial intelligence is taking over many aspects of diagnosis and care. This is counter-intuitive. One would think that something like healthcare is so varied and so complex, that it needs a human’s judgment to make sound decisions. In reality, this variation and complexity is the precise reason machines are doing so well.
Take this example: Innovation is similarly accelerating in imaging techniques, medicine, medical devices and other aspects of healthcare. To keep up, big hospitals and small physicians practices alike rely on prescribed Clinical Pathways to decide on course of treatment. There are many platinum standard organizations that publish these pathways. For example, US Oncology and National Comprehensive Cancer Network are two of the most reputed ones in United States just for cancer. Such pathways are usually updated every quarter.
The complexity of healthcare makes it an ideal use case for application of Artificial Intelligence. There is much supervisions and many redundancies – you are in safe hands.
If you put payer’s in the mix, whether insurance companies or government agencies, the complexity explodes, because payer’s may prescribe to their own set of pathways. As a result each payer-provider pair negotiate a custom set of rules. Of course, these vary by every state to account for different regulatory policies. In other words, a simple thing like getting a procedure pre-approved by an insurance company, becomes a nightmarish task for both, the provider and the payer.
In such situations, human judgment is limited in its efficacy at best. Here even classic artificial intelligence or machine learning techniques have not been that successful. Traditional machine learning needs homogenized sets of training data for each specific training model, no easy task with this kind of fragmentation.
Still, machines will take over this process very soon. With technologies like tactical cognitive computing, even such fragmented use case can be modeled with 95-98% accuracy. Instead of programming a massive black box, a tactical cognitive computing solution would model each of the pathways and contracts in a way that can be seamlessly updated, and then brings them together to automate the workflow.
Some of you are thinking that 98% accuracy is not high enough for something like healthcare, and you are right. Don’t worry, there are enough human supervision, redundancies, and checks in the system. Machines are taking over the world of healthcare, but you still are in safe hands.